ADVANCES IN INFORMATION SCIENCE

Towards a Comprehensive Model of the Cognitive

Process and Mechanisms of Individual Sensemaking

Pengyi Zhang

Department of Information Management, Peking University, 5 Yiheyuan Road, Haidian District, Beijing,

100871, China. E-mail: pengyi.zhang@pku.edu.cn

Dagobert Soergel

Department of Library and Information Studies, Graduate School of Education, University at Buffalo, 534 Baldy

Hall, Buffalo, NY 14260. E-mail: dsoergel@buffalo.edu

This review introduces a comprehensive model of the

cognitive process and mechanisms of individual sense-

making to provide a theoretical basis for:

• empirical studies that improve our understanding of the cog-

nitive process and mechanisms of sensemaking and integra-

tion of results of such studies;

• education in critical thinking and sensemaking skills;

• the design of sensemaking assistant tools that support and

guide users.

The paper reviews and extends existing sensemaking

models with ideas from learning and cognition. It

reviews literature on sensemaking models in human-

computer interaction (HCI), cognitive system engineer-

ing, organizational communication, and library and

information sciences (LIS), learning theories, cognitive

psychology, and task-based information seeking. The

model resulting from this synthesis moves to a stronger

basis for explaining sensemaking behaviors and con-

ceptual changes. The model illustrates the iterative pro-

cesses of sensemaking, extends existing models that

focus on activities by integrating cognitive mechanisms

and the creation of instantiated structure elements of

knowledge, and different types of conceptual change to

show a complete picture of the cognitive processes of

sensemaking. The processes and cognitive mecha-

nisms identified provide better foundations for knowl-

edge creation, organization, and sharing practices and a

stronger basis for design of sensemaking assistant

systems and tools.

Introduction: Overview and Scope

Information science research and practice, including

librarianship, has made much progress in understanding

information behavior, especially information seeking, and in

helping users to find and access information. Information

retrieval has seen large advances, even though much still

needs to be done to assist users with conducting meaningful

searches; information and communication technologies

provide access to huge amounts of information. Likewise,

information-literacy education has focused on information-

search and evaluation skills. But use of the information found

and assisting users with such use has received less attention

from system builders and information-literacy educators.The

next frontier is building systems that assist users with making

sense of all the information found and to educate students and

users generally in the best ways of using and applying infor-

mation, with or without the use of such next-generation

systems. We need to bring together concepts and techniques

from information science (broadly construed to include rel-

evant areas of computer science), information design and

visualization, education, instructional design, and learning

theory to build systems and services that assist users with

and prepare them for learning and sensemaking in an

information-rich world (Neuman, 2011a,b).

As a contribution to this effort, this review introduces a

comprehensive model of the cognitive process and mecha-

nisms of individual sensemaking to improve our understand-

ing of sensemaking and to provide a theoretical basis for:

• Empirical studies of the cognitive process of sensemaking and

integration of the results of such studies

• Education in critical thinking and sensemaking skills

Received September 5, 2012; revised August 21 2013; accepted August 23,

2013

(wileyonlinelibrary.com). DOI: 10.1002/asi.23125

JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY, 65(9):1733–1756, 2014

• The design of sensemaking assistant tools that support and

guide users

Sensemaking is the information task of creating an under-

standing of a concept, knowledge area, situation, problem,

or work task (application task; for definitions of information

task and work task, see Ingwersen & Järvelin, 2005;

Byström & Hansen, 2005) often to inform action. Sense-

making is a prerequisite for many work tasks such as

problem solving, decision making, planning, and executing

a plan. Sensemaking has also been defined as:

A process of forming and working with meaningful represen-

tations in order to act in an informed manner (Stefik et al., 1999;

Pirolli & Russell, 2011).

and as:

Reading into a situation patterns of significant meaning

(generalized from the original quote: individuals “realize their

reality, by reading into their situation patterns of significant

meaning; Morgan, Frost, & Pondy, 1983, p. 24”).

An important part of sensemaking involves making clear

the interrelated concepts and their relationships in a problem

or task space.

As an example, consider a business analyst who is devel-

oping a marketing plan for a product, including a TV ad

campaign and advertisements on other media such as print,

radio, and the web. To understand the whole picture, she/he

needs to gather information about the product and its competi-

tors; conduct thorough research in trade and business presses

and periodicals about the product, the company, and its com-

petitors, potential users, and market shares and trends; and

then develop multiple ideas for the marketing plan based on

the research. The analyst is involved in a sensemaking task.

Sensemaking (Brenda Dervin, the originator of the sense-

making methodology, prefers the spelling with a hyphen

while the community in computer science and more techni-

cal people in information science [for example, SIG-CHI]

use sensemaking without a hyphen) is a series of continuing

gap-defining and gap-bridging activities between situations

(Dervin, 1992, 1998). The sensemaker accomplishes each

sensemaking “moment” by defining and dealing with the

situation, the gap, and the bridge.

Sensemaking may be carried out by an individual or in a

group or organization or it may be a societal endeavor. It

involves both cognition and affect and interpersonal relation-

ships and group/organizational dynamics. Dervin and

Naumer (2010, p. 4696) distinguish work on sensemaking in

four fields: “Human Computer Interaction (HCI) (Russell’s

sensemaking); Cognitive Systems Engineering (Klein’s sen-

semaking); Organizational Communication (Weick’s sense-

making; Kurtz and Snowden’s sense-making); and Library

and Information Science (Dervin’s sense-making).” As can

be seen from Figure 1, sensemaking by individuals or groups

takes place in a rich context and has many influences. This

paper focuses on sensemaking by individuals and within

that on cognitive processes, drawing on literature from

human-computer interaction and cognitive systems engineer-

ing. Further, the paper addresses primarily conscious, delib-

erate,

and

often

somewhat

systematic

processes

sensemaking that usually involve deliberate search for and

use of information; but less conscious, experiential sense-

making falls within the general parameters of the sensemak-

ing models presented. Parts of the model may apply to

components of organizational sensemaking as well. In the

remainder of the paper, sensemaking is used to refer to this

limited scope.

There are several areas and literatures that overlap with

sensemaking and that present models that are similar to the

sensemaking models we discuss later in this paper: Informa-

tion seeking and learning (we include a few representative

models of both), research methods (which we have treated as

kind of sensemaking and included in our review of models),

creativity (Kaufman & Sternberg, 2010), problem solving

(the classic Polya, 1945/1957/2004, Problem solving, n.d.,

and, as an example, one of many how-to books, Treffinger,

Isaksen, & Stead-Dorval, 2005), and the many books on

writing scholarly papers, theses, etc. Including models from

these areas would have enriched our analysis of sensemaking

and related activities (see Table 1 and the SupportingAppen-

dix), but is well beyond the scope of this paper.

In the literature and in this paper, sensemaking has both a

broad and a narrow meaning. Sensemaking, broadly defined,

refers to the overall process of the following two subprocesses:

1. Seeking information, followed by extracting and filtering

the information found; also called sensing.

2. Sensemaking, narrowly defined, which refers to itera-

tively creating and updating an understanding of the situ-

ation, especially connections (for example, between

people, places, events, intentions, etc.), creating a repre-

sentation

that

can

support

decisions

effective

action (Klein, Moon, & Hoffman, 2006a). This can be

FIG. 1.

The Sense-making metaphor (copyright, Brenda Dervin 2010 as

published in Dervin & Foreman-Wernet, 2012, Figure 1, p. 156). Reprinted

with permission.

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TABLE 1.

Elements of sensemaking.

Sensemaking is an iterative process that can include many activities in different sequences. They may iterate in several rounds until the goal is reached, intertwine with and

influence each other, or spiral up to the final presentation. The activities are often defined by a combination of several characteristics. Different sensemaking models

describe these activities at different levels of granularity. This figure presents a (faceted) classification of the activities and characteristics found in the models we reviewed.

0 General control and command activities applied at individual steps throughout the process

0.1 Planning − .2 Defining − .3 Evaluating − .4 Interacting − .5 Feedback − .6 Monitoring − .7 Reflecting

A Task analysis and determination of information gaps

Primarily for work tasks but also meta information tasks and learning tasks

A0 Selecting a topic (for example, for an assignment or for research)

A1 Preparing. Becoming familiar with the general or specific area of the work task / problem

A2 Task / problem definition (could be considered part of developing the structure)

A3 Identifying audience − A4 Establishing evaluation criteria for the research product and process.

B Information Seeking. Searching for information / data / structure

B0 Planning and carrying out the search process

B0.1 Determining information needs − .2 Search strategy − 2.1 Conceptual query formulation − .2.2 Selecting and sequencing sources. − 2.3 Source-specific query

formulations − .3 Executing search strategy − .4 Reviewing results − .5 Editing results. − .6 Checking helpfulness of the results

B1 Searching for data versus Searching for structure

B1.1 Searching for data

B1.1.2 Using structure to find data − .1 Searching for data guided by structure −.2 Inferring data

B1.2 Searching for structure (as contrasted with building structure) = C1.1.1.3

B1.2.1 Searching for structure based on problem / gap definition

B1.2.2 Search or activation of structure prompted by data

B2 Scanning the environment versus specific search

B3 Exploratory (pre-focus) versus focused search

B4 Searching for sources versus extracting data or structure from sources

B5 Searching in outside sources versus search in a data store or representation created for the sensemaking problem at hand

B6 Evaluating and selecting information. Judging quality and relevance NT B0.4, B0.5

C Making sense of the information / data: Analyzing and synthesizing the data. Creating a representation that fits the data into a structure or schema

At the heart of sense making is the interplay of structure and data.

C1–C3 approximate an umbrella process sequence.

C4–C8 give many characteristics of activities in the interplay of data and structure arranged into a faceted classification

C1–C3 Approximate an umbrella process sequence

C1 Building / acquiring and instantiating structure

C1.1 Building or acquiring structure or structure modification

C1.1.1 Mode of building or acquiring structure or structure modification

C1.1.1.1 Building new structure or modifying structure

C1.1.1.1 : C1.1.2.1

Building structure inductively from the data

C1.1.1.2 Activating existing structure that is close at hand (in memory, external store)

C1.1.1.3 Finding structure through search in outside sources = B1.2

C1.1.2 Basis for building or acquiring structure (combine with C1.1.1)

C1.1.2.1 Building or acquiring structure from data or prompted by data

C1.1.2.2 Building or acquiring structure from problem definition

C1.1.2.3 Building or acquiring structure from theory

C1.1.2.4 Building or modifying structure influenced by predisposition and purposes

C1.1.2.5 Building or modifying structure influenced by past experience

C1.1.3 Comparing to other structure

C1.1.4 Format of resulting structure (for example, a list of hypotheses)

C1.2 Instantiating structure / making data and structure fit. Organizing information

C1.2.2 Fitting data into the structure − .2 Discarding data −.3 Using structure to filter data

C1.3 Stage of Building / acquiring / instantiating structure or structure modification

C1.3.1 initially versus.2 Modifying, revising, replacing structure = C2.2

C2 Examining and revising structure. Determining whether completed RT C3.2

Strictly speaking, all part of C1 (in iteration); broken out for easier correspondence to other models

C2.1 Examining and questioning data and structure

C2.1.1 Examining data not yet fitted. leftover non-fitting data (residue) versus new data

C2.1.2 Tracking anomalies − .3 Detecting inconsistencies. − .4 Judging plausibility

C2.1.5 Gauging data quality (may be informed by structure)

C2.2 Modifying, revising, replacing structure

C2.2.1 To fit residue of non- or ill-fitting data, “representational shift” −.2 To fit new data

C3 Formulating the result in a report/presentation for a specific audience

May include recommendations for action, already starting D

The sense-making process may continue in this step as problems surface in the writing

This could also be a “report to self”, as an internal or external representation

C3.1 Preparing draft report/presentation

C3.2 Reflecting on the process so far and the resulting product

C3.3 Revising / refining report/presentation as needed

C3.4 Disseminating report/presentation

C4–C8 Further characteristics of activities in the interplay of data and structure

C4 Mechanisms for conceptual change. Sensemaking mechanisms

Data-driven or bottom-up versus structure-driven or top-down (Section Sensemaking Mechanisms)

C5 Degree of structure change when fitting data into structure (Section Types of Conceptual Change)

C6 Internal versus external representation

C7 Granularity of iterations by amount of information processed

D Consuming the instantiated structure. Applying the results to the work task: making a decision, executing an action, etc., by the sensemaker or the reader/listener

of a presentation

E Feedback

E1 Evaluative feedback. Did the report/presentation provide guidance for action? Action successful?

E2 Requirement feedback: Additions or changes to requirements

E3 Data feedback: New or corrected data found in application

E4 Structure feedback: Changes in the structure discerned in application

F Reflecting on the process and its results. Considering lessons learned. Updating individual and group store of knowledge, internal and/or external

JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY—September 2014

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accomplished by relating information found to previous

knowledge, creating structures, and fitting data into struc-

tures to create representations, thus arriving at an under-

standing of a situation or phenomenon (Russell, Stefik,

Pirolli, & Card, 1993). Also called making sense.

Pirolli and Card’s (2005) model shows this broad and

narrow use very clearly (in the discussion of models,

Figure 5). The outer loop encompassing all activities is

labeled “Sense-Making Loop for the Analyst”; it includes an

inner loop which contains the activities for the narrow

meaning and is called “Sense-Making Loop.” It is generally

clear from the context which meaning is intended.

The first steps in sensemaking, broadly defined, are deter-

mining an information gap and seeking information; sense-

making and information seeking and use are closely

intertwined in reality and commingled in terminology. Just

as sensemaking, broadly defined, includes information

seeking, so does information seeking and use include sense-

making, narrowly defined (in the use part). Thus, sensemak-

ing, broadly defined, and information seeking and use refer to

the same total process but emphasize different subprocesses.

Information seeking and use can be viewed as a continuum of

various levels of sensemaking, ranging from fitting informa-

tion directly to its need (for example, a task like catching a

plane that can be supported with a factoid answer—the depar-

ture time) to very complex sensemaking activities illustrated

at the beginning of the paper. Sensemaking is particularly

important when people are faced with new situations and

complexity in work tasks such as decision making, strategy

development, and policy making (Kurtz & Snowden, 2003).

Previous research on sensemaking, broadly defined, has

examined the information-seeking aspects extensively, while

research on the construction of the knowledge representations

has been by and large descriptive. Several important questions

about the creation and updating of the knowledge representa-

tions and about the cognitive process and mechanisms behind

such changes still go unanswered. Research in learning and

cognition and in task-based information-seeking and use

behaviors all bring useful insights to sensemaking research.

Through the development of a theoretical framework and a

comprehensive sensemaking model this paper aims at provid-

ing some insight into the unanswered questions.

The purpose of this paper is to construct a comprehensive

model of the cognitive process and mechanisms of indi-

vidual sensemaking by fitting together components from

sensemaking models, learning theories, and cognitive psy-

chology, as they discuss sensemaking mechanisms. Table 1,

itself a product of a sensemaking process, provides a listing

of all the elements from the sensemaking and learning

models discussed in the following in an integrated structure;

thus, it provides a useful framework for the description and

discussion of these models. The reader may also want to

look ahead at the complete model in Figure 17 as a context

for the discussion. The Online Appendix (www.dsoergel

.com/ZhangSoergel2013Appendix.docx) has a full version

of Table 1 (TABLE A-1) and two additional tables: Table

A-2 lists for each model the activities and gives for each

activity the combination of characteristics from this classi-

fication; Table A-3 gives for each element of the classifica-

tion the corresponding activities from all models.

We base our analysis on a selection from the sensemaking

and related literatures. We aimed to include all major sense-

making models. First we identified the interdisciplinary areas

where research on sensemaking has been done, including

HCI, cognitive system engineering, organizational commu-

nication, and library and information science (LIS). We then

examined each area and identified major sensemaking

models, whether so labeled or not, paying particular attention

to models that added new features. We cast a wider net to

include practical task areas for which sensemaking is dis-

cussed most often, for example, intelligence analysis, orga-

nizational practice, and criminal investigation, to add models

we found useful. We decided not to include sensemaking

models that did not further contribute to the formation of our

comprehensive model (as the information-seeking and learn-

ing models did), for example, Snowden’s multi-ontology

sensemaking (Snowden, 2005), Vivacqua and Garcia’s sen-

semaking state diagram (Vivacqua & Garcia, 2009), and Wu

et al.’s spatial sensemaking model (Wu, Zhang, & Cai, 2010).

As mentioned earlier, we consider conducting research and

most of learning as forms of sensemaking; we also wanted to

acknowledge the importance of information search for all

these activities. So we added representative search and learn-

ing models selected for their intellectual appeal and their

contribution to our overall model. For learning and cognition

theories, we selected theories or models relevant to either the

overall process or any components of sensemaking and useful

in explaining the cognitive process and mechanisms inside

the process and activities.

We used conceptual analysis to identify the main com-

ponents of the sensemaking process. For each sensemaking

model we identified the components (often referred to as

“processes” or “steps”) and how the components are com-

bined (often referred to as “loops,” “cycles,” or “phrases”).

Then we compared the components across all sensemaking

models, identified processes that are common to many and

sequences of processes that appear repeatedly; we also rec-

ognized variance in steps and sequences of sensemaking.

The remainder of the paper is organized as shown in

Figure 2: We first review relevant theoretical and empirical

research in many areas that contribute to our framework or

model and then describe the comprehensive model. We con-

clude with a discussion of future sensemaking research

directions. This paper is based on the first author’s disserta-

tion (P. Zhang, 2010) with appropriate updates.

Contributions From Sensemaking Models in HCI,

Cognitive System Engineering, Organizational

Communication, and Library and Information

Sciences (LIS)

Several sensemaking models have been proposed for dif-

ferent purposes, such as:

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• To provide an analytical abstraction derived from empirical

user studies (Russell et al., 1993; Klein et al., 2006b; Qu,

2003; Qu & Furnas, 2008);

• To describe the sensemaking processes, either of particular or

generic user groups (Krizan, 1999; Pirolli & Card, 2005).

The existing models are by and large descriptive of the

activities and processes involved in individual or collabora-

tive sensemaking and do not explain the cognitive mecha-

nisms the sensemakers use (see sections Contributions

From Learning Theory and Contributions From Cognitive

Psychology).

Norman and Bobrow’s Model

This model comes from cognitive psychology but is quite

similar to models in HCI. Figure 3 shows the schemata

representation of the human information-processing system.

Physical signals in the external representation get through

the sensory system to become the data pool from which

schemata are constructed to support communication and

decision making (Norman & Bobrow, 1976).

Russell’s Sensemaking Model

Through several case studies, Russell et al. (1993)

explored the cost structure of sensemaking. They identified

four main processes in sensemaking, shown in Figure 4.

1. Search for representation (structure or schemas; genera-

tion loop): the sensemaker creates both representations

and the procedures that use them.

2. Create instances of representations (data coverage loop):

the sensemaker identifies information of interest and

encodes it in (fits it into) the representation.

3. Modify representation (representational shift loop): when

some data do not fit well (or not at all) into the represen-

tation,

the

sensemaker

modifies

the

representation

(restructuring).

4. Consume instantiated schemas: the sensemaker uses the

instantiated schemas in the task-specific information-

processing step.

The schemas provide top-down or goal-directed guid-

ance, by prescribing what to look for in the data, what

questions to ask, and how the answers are to be organized.

Structural representation plays a crucial role in all pro-

cesses. The generation loop represents the construction of a

structural representation; the data coverage loop represents

the fitting of data or evidence into the structure. When there

is a mismatch between the representation and the data, a

residue of data that do not fit remains, and the representa-

tional shift loop takes place to reconstruct the representation.

As presented, Russell et al.’s model starts with representa-

tion, that is, in the structure-driven mode. The initial

representation could also be generated from the data (data-

driven). In either case, the representation is checked against

the data and modified as needed.

Pirolli and Card: Notional Model of Sensemaking

Through cognitive task analysis (Chipman, Schraagen, &

Shalin, 2000), Pirolli and Card (2005) proposed a detailed

model of the steps in the sensemaking process, the variables

involved, and a general flow of how they interact. Their

model was developed to describe the work of intelligence

analysts where the product of sensemaking is a presentation

or report. The overall flow of sensemaking follows the path:

Information → schema → insight (hypotheses) → product

FIG. 2.

Organization of the paper.

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Cognitive task analysis (CTA) identified two loops of

activities:

• An information “foraging loop” that involves searching for

documents in external data sources and filtering them for

relevance and then “reading and extracting information into

some schema,” and

• A “sensemaking loop” that uses the schema to iteratively

“develop a mental model (conceptualization) that best fits the

evidence”: The schema that aids analysis; the development of

insight through the manipulation of this representation; and

the creation of some knowledge product (a presentation) or

direct action based on the insight.

This process starts out in a data-driven mode, but the

analyst often goes back to a previous step as shown in

Figure 5 through arrows in both directions. Figure 5 illus-

trates 10 processes and six representations (ranging from

external raw data to the final task presentation) of the sen-

semaking process for intelligence analysts. External data

sources are raw evidence, largely in textual form. The

“shoebox” is a much smaller subset of the external data that

is relevant for processing. The evidence file refers to snip-

pets extracted from items in the shoebox. Schemas are the

re-representation or organized marshaling of the information

so that it can be used more easily to draw conclusions.

Hypotheses are the tentative representation of those conclu-

sions with supporting arguments. Ultimately, there is a

representation in the sensemaker’s mind or externalized in a

report, based on which the sensemaker or (most likely in the

intelligence context) the recipient of the report can make a

decision and execute an action.

The Data/Frame Theory of Sensemaking

Klein et al.’s (2006b) data/frame theory (Figure 6) elabo-

rates on Russell’s data coverage loop and the representation

loop as the two major cycles of sensemaking activities,

including:

1. An elaboration cycle where the sensemaker elaborates

the structural representation (frame) by adding detail,

FIG. 3.

The memory schemata view of the human information-processing system (Norman & Bobrow, 1976, Figure 6.2, p. 118). Reprinted with

permission.

FIG. 4.

Russell’s sensemaking model (Russell et al., 1993, p. 271).

Reprinted with permission.

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intelligence creation and use (in government or business)

follows a series of repeated and interrelated steps (Krizan,

1999). Each step adds value to the inputs and together they

create a substantially updated report. The analysis (sense-

making) processes convert “information” into “intelligence”

for planners and decision makers.

This model focuses on five critical steps, again in a data-

driven mode (Figure 7):

1. Planning/tasking: intelligence needs are assigned or pro-

vided by customers (end users) to analysts. Intelligence

needs are often complex and time-sensitive. Intelligence

analysts need to interpret the customer requirements

before the task can be processed.

2. In the collection step, analysts acquire information from

various

sources,

including

people

and

information

systems.

3. Processing is the selection of raw information based on its

plausibility, expectability, and support to intelligence

issues.

4. In the analysis step, analysts try to make sense of the

selected information and make higher-level analyses

including giving descriptions of the task domain, estab-

lishing explanations of phenomena, and interpreting

cause and effects.

5. In the production step, an intelligence report giving

“value-added actionable information” is created by syn-

thesizing the information extracted from all available

sources, including the intermediate products of previous

steps, to create a comprehensive assessment of an issue or

situation.

The intelligence report is disseminated to the customers

for action and for evaluation and feedback; a next round of

sensemaking activities may follow.

Barrett’s (2009) Sensemaking in Criminal Investigation

Criminal investigation and intelligence analysis share

similar task complexity and data incompleteness. Making

sense of terrorist or criminal actions involves generating,

elaborating, and testing alternative plausible hypotheses,

which is representative of complex sensemaking tasks.

Investigative sensemaking activities (Figure 8) involve

explaining the event behind the data collected, speculating

about other possibly related events, and inferring plausible

hypotheses that explain the situation. The sensemaker (such

as a jury or detective) tries to create a meaningful under-

standing of the evidence gathered through witnesses, exhib-

its, and arguments at trial. Through constructive mental

activity, possibly recalling earlier cases, the investigator

creates one or more stories to interpret the evidence

(Pennington & Hastie, 1991). (The stories are the structure

in this case; this is initially a data-driven process.) The

model describes the interaction of knowledge structure with

investigative evidence in the environment to inform the

generation and testing of stories/hypotheses.

This model adds a second way in which knowledge struc-

ture and data interact: The knowledge structure (activated by

investigative goals) guides data collection. Some of the data

collected may in turn activate knowledge structures already

held by the sensemaker, and these knowledge structures

added to the active set in turn guide the collection of still

other kinds of data—an iterative process. The important

points here are: (a) Data collection is not a “neutral” process;

the attention of the sensemaker is focused and guided by

knowledge structures active in the sensemaker’s mind; (b)

When data do not fit the active structure(s), the sensemaker

can either change the structure (as in Russell’s and many

other models) or he/she can activate another structure he/she

already knows to accommodate the new data. Such activa-

tion may also occur when a single data item is discovered.

For example, when it is discovered that a member of a group

suspected of terrorism is a specialist in making nail bombs,

the structure for bombing at public gatherings is activated

and guides the collection of a whole different set of data. If

the sensemaker does not have the needed structures at hand

this may trigger a search for structure (see Qu & Furnas,

2008).

Among the sensemaking processes illustrated (investiga-

tion, evaluation, and conclusion), the essential part, investi-

gation of cues and knowledge via mental representations,

includes activities such as:

• Generation of structure (hypotheses) to explain data

• Evaluation of completeness and coherence of structure and

mental representation

• Evaluation of conflict within mental representation (alterna-

tive hypotheses)

FIG. 7.

The cyclical model of the intelligence process (Krizan, 1999).

Reprinted with permission. [Color figure can be viewed in the online issue,

which is available at wileyonlinelibrary.com.]

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Louis (1980): Sensemaking in Organizational Entry

When a newcomer enters an organization, he or she needs

to make sense of the new environment. Louis (1980) iden-

tified several inputs to such sensemaking, including other’s

interpretations, local interpretation schemes, and predispo-

sitions and purposes and past experiences (Figure 9). These

inputs enter the sensemaker’s interpretation process to attri-

bute meaning, which leads to responding actions and an

updated understanding. Data collection occurs in the process

of interaction with others in the organization. Sensemaking,

building a structure and understanding of how the organiza-

tion works, occurs incrementally, with small updates often

immediately guiding action in the moment.

Organizational Sensemaking

The study of organizational sensemaking has identified

the same pattern of sensemaking as an iterative process of

(a) searching and (b) sensemaking, narrowly defined (Weick,

1995; Weick, Sutcliffe, & Obstfeld, 2005; Choo, 1998,

2006). Choo (1998, 2006) frames the organization’s adapt-

ability in a dynamic environment into a twofold challenge:

sensing and making sense. Sensing means noticing poten-

tially important messages in the environment—acquiring

information about events, trends, and relationships in an

organization’s external environment in which every part is

interconnected with other parts in complex and unpredict-

able ways. Making sense means constructing meaning from

FIG. 8.

Investigative sensemaking model (Barrett, 2009, Figure 1.2, p. 24). Reprinted with permission.

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the data about the environment that have been sensed. The

challenge for sensemaking is that there are multiple interpre-

tations. Organizational sensemaking is inherently a “fluid,

open, disorderly, social process” (Choo, 1998, p. 67).

Conducting Research as Sensemaking

Conducting research is a sensemaking process. Research-

ers start with a lack or discontinuance of knowledge, recog-

nize the gaps to be filled (research questions), and conduct

research using various methods to bridge the gaps.

Compared to sensemakers in other scenarios, researchers

undertake a more systematic approach to identifying gaps;

they explain specifically what they attempt to learn or under-

stand, explicitly articulating a research topic and one or

more research questions and subquestions (Creswell, 2003;

Maxwell, 2005). Researchers then (a) collect data (search)

and (b) analyze and interpret the data (sensemaking, nar-

rowly defined).

Often the data do not exist anywhere to be “retrieved”

(exceptions include secondary analysis, meta-analysis, and

analysis of historical documents); researchers need to collect

data in experimental or natural settings. Quantitative

and qualitative methodologies take different approaches to

making sense, answering the research questions. In general,

the quantitative approach is structure-driven (deductive, or

top-down), starting with a hypothesis, collecting experimen-

tal data (often in controlled conditions), and conducting

statistical analysis to test if the data support the hypothesis

(Kirk, 1995); however, there are also data-driven quantita-

tive approaches such as exploratory data analysis and data

mining. The qualitative approach, on the other hand, may

use a combination of deductive and inductive data analysis

(Potter, 1996). For example, a researcher may use the

grounded theory approach, “grounded” in the data (data-

driven) and developing increasingly higher-level concepts

and theoretical models (Denzin & Lincoln, 2003). During

this process new questions often emerge—data activating

structure or leading to the construction of new structure.

The final product of research is usually a presentation

describing the data and the sense made (findings and con-

clusions). Several intermediate products such as coding

schemas, case reports, and researcher notes may be pro-

duced to assist sensemaking.

Research as sensemaking should also be seen in a larger

context, not confined to individual studies but to the research

program of a person or group or research enterprise in

a subject domain. Much research is exploratory, creating

structure to guide further research.

Qu and Furnas (2008): Structural

Information-Seeking Model

A common theme of the comprehensive models dis-

cussed in this section is the interaction between structure/

frame and data. Qu and Furnas (2008) focus on a

FIG. 9.

Inputs of sensemaking in organizational entry (Louis, 1980). Reprinted with permission.

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subprocess, searching for structures. They highlight the need

for finding structure as an information need in its own right

when the representation is inadequate, incomplete, or ill-

formed and the need for changing, growing, or validating

structures arises or when a piece of data “calls for” a struc-

ture into which it fits. This is related to the observation by

Barrett (2009) that pieces of data may activate structures,

except that they think of structures that the sensemaker

already has at hand while Qu and Furnas (2008) talk about

new structures to be acquired. Qu and Furnas analyzed and

developed a model for the process of seeking structures

(Figure 10).

Information-Search and Use Models and Learning Models

In this brief section, we present, by way of comparison,

representative models of information search and use and

models of learning. Together they roughly describe the same

overall process as defined in the broad meaning of sense-

making from a different perspective.

Soergel’s (1985) search process model (Figure 11) is a

very general normative model. Kuhlthau’s (1991, 1993,

2004) information-search process (ISP) model (Figure 12)

was developed primarily in the context of students’ search-

ing for information needed for school assignments; it

identifies the affective, cognitive, and physical behaviors.

Nesset’s (2013) preparing, searching, and using (PSU)

model (Figure 13) captures the information-related activities

in the conduct of an instructional unit in a school. It

identifies positive and negative feelings that accompany the

activities in each stage. It adds the preparation stage, which

is useful in general, not just for students. Neuman’s

(2011a,b)

I-LEARN

model

(Figure 14)

describes

the

process of learning with information. It is a normative model

that draws on information-seeking behavior models and

theories

learning/instructional

design. The Alberta

Inquiry Learning Model (Alberta Learning. Learning and

Teaching Resources Branch, 2004; Figure 15) was devel-

oped as a guide for learning and instruction.

Summary

Sensemaking is an individual or collective construction

of knowledge. The models discussed in this section all

attempt to describe the process whereby knowledge is

created. Despite the differences, the basic pattern of iterative

interaction between search for data and creating a structure

that makes sense of the data (see Figure 17) is in common to

all models. They differ in detail, in what aspects they empha-

size in defining subprocesses, and often in the sequence of

steps. Table 2 compares different sensemaking models and

illustrates the common processes.

Several important points are raised in the sensemaking

literature:

Sensemaking is comprised of iterative information

seeking (the acquisition of data, sensing) and sensemaking

(the creation of a knowledge structure, making sense)

processes. For the information-foraging/seeking loop, the

evolution of a user’s interests depends upon the changing

characteristics of the information context. New information

gives users new ideas and directions to follow. Users use

FIG. 10.

Structural information seeking (Qu & Furnas, 2008). Reprinted with permission. [Color figure can be viewed in the online issue, which is available

at wileyonlinelibrary.com.]

FIG. 11.

General search process model (Soergel, 1985, p. 343). Reprinted

with permission.

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information from the current situation to decide where to go

next (Bates, 1989; Ingwersen & Järvelin, 2005). Researchers

have identified the important role of exploratory search and

developed systems to support it (Baldonado & Winograd,

1997; Qu, 2003). The sensemaking loop, on the other hand,

including activities such as summarizing, organizing, and

identifying patterns, is not as well supported. A key task in

sensemaking is to identify patterns of concepts and relation-

ships to build on.

Data and structure play different roles in creating knowl-

edge representations. Russell et al.’s model (1993) illus-

trates that the major cost of sensemaking is related to the

structural representation, including the cost of finding or

building a representation to support required operations in

the target task, and the cost of instantiating the representa-

tions. Research indicates a separation of data and structure

in sensemaking tasks and the use of external representations

for tasks where information processing is complex (J.

Zhang, 1997, 2000).

Data and structure are intertwined. The sensemaker may

select and activate structures based on the knowledge

required for the work task, either from the sensemaker’s

memory or through search for structure. Active structures

may guide the acquisition of data. Or, the sensemaker

acquires data largely unguided. Data may lead to structure in

two ways: A piece or configuration of data may activate an

existing structure—from the sensemaker’s memory or

acquired through search for structure (for example, based on

cue extraction, Seligman, 2006) or the sensemaker creates a

new structure that accommodates the data. Regardless of

how the structure is created and activated, the sensemaker

tries to fit data into active structures (instantiate the

FIG. 12.

Model of the information-search process (Kuhlthau, 2004, p. 82). Reprinted with permission. [Color figure can be viewed in the online issue,

which is available at wileyonlinelibrary.com.]

FIG. 13.

Model of the information-search process (Nesset, 2013, p. 100). Reprinted with permission.

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structures data); put differently, the sensemaker tests the

structure against data by judging plausibility and detecting

inconsistencies (Klein et al., 2006b). Data that do not fit lead

the sensemaker to modify structures or activate additional

existing structures. In short, structure guides data acquisi-

tion, sensemakers fit data into structures, nonfitting data may

activate existing structures or lead the sensemaker to build or

update structures. Thus, sensemaking does not always have

clear beginning and ending points. The simplified waterfall

or cyclical model rarely applies; rather, there is often a

back-and-forth between several steps before moving on,

as shown through empirical evidence about several sense-

making tasks, for example, expert decision making (Klein

et al., 2006a).

To summarize, previous research has identified important

processes involved in sensemaking, involving the interre-

lated activities of the search for data and the creation of an

understanding. The importance of structure associated with

the processes is recognized: not only do structures influence

how people seek for information, they are critical to the

creation of an understanding. It seems quite clear that sen-

semakers seek structures in sensemaking tasks and their

sensemaking processes are closely related to the structural

representation of task situations. However, the sensemaking

models discussed in this section are by and large descriptive

of the activities and processes involved in individual or

collaborative sensemaking; they do not delve into the cog-

nitive process and mechanisms that contribute to the cre-

ation, modification, and update of structures when existing

external structures are not available or ready to use. They do

not address questions such as:

• What types of conceptual changes occur during the sensemak-

ing process and how do they occur?

• What mechanisms trigger the changes and enable the assimi-

lation of new information and the creation of a structural

representation?

• What are the cognitive processes and structures by which

knowledge is created and stored during the sensemaking

process?

This paper aims to enhance existing sensemaking

theories by incorporating ideas from learning and cognition

to gain a more in-depth understanding of sensemaking.

Contributions From Learning Theory

Learning is more than the collection of inputs and the

production of outputs. The mind has the ability to extract,

analyze, synthesize, and formulate received information

and stimuli and then to produce abstractions and represen-

tations that go beyond what can be directly attributed to the

input given (Gredler, 2008). Much learning is sensemaking,

especially using recorded information or systematic discov-

ery to learn concepts, ideas, theories, and facts in a domain,

such as science or history. We do not deal with rote learning,

drill, and practice for mental or physical skills, or other

forms of learning where sensemaking is not central.

TABLE 2.

Elements in selected sensemaking models (the sequence of elements in the model may differ).

Dervin, 1992, 1998

Krizan, 1999

Pirolli & Card, 2005

Russell et al., 1993

Qu and Furnas, 2008

Klein, et al., 2006b

Maxwell, 2005;

Kirk, 1995

Task analysis

Gap identification

Task planning

Research goal and

questions

Search

Exploratory search

May include both gap

identification and

gap bridging

Data collection

Search for & filter

information

Determine structural

information need

Recognize a frame

Search for theoretical

framework

Focused search

• Finding sources

• Extracting data &

structure

Search for evidence

Search for

representation

Search for structures

Elaborate a frame

Data collection

Sense-making

(narrow meaning)

Fitting data into

structures

Interpretation and

analysis

Search for support

Create instances of

representations

Define, connect and

filter the data

Data analysis

Building structures

Schematize

Search for

representation

Explore / identify

extract structure

Construct and

question a frame,

reframe

Updating knowledge

Gap bridging

Build-case

Consume instantiated

schemas

Change, grow,

validate structure

Preserve a frame or

reframe

Interpretation &

conclusion

Reevaluate

hypotheses

Modify representation

Preparing

task output

Production and

dissemination

Tell story

Writing papers,

reports, or books

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Experiential learning, while not the focus of this section,

falls within the broad parameters of the learning models

presented earlier and the learning theories reviewed here. In

this section, we review learning theories that elaborate

on sensemaking and the types of conceptual changes that

occur in sensemaking. Learner and sensemaker are used

interchangeably.

Learning Theories as Theories of Sensemaking

All the learning theories that will be discussed here

revolve around the same basic idea: fitting new information

into an existing or adapted structure; they differ in emphasis

and nuance. For a summary comparison of learning theories,

see Grabowski (1996).

Assimilation

theory

(theory

meaningful

learning)

(Ausubel, Novak, & Hanesian, 1978).

In meaningful learn-

ing a new piece of information is assimilated to an existing

relevant aspect of the learner’s knowledge structure:

• The development of new meanings is built on prior knowl-

edge, that is, relevant concepts and relationships. Structure

plays a crucial role in meaningful learning.

• When meaningful learning occurs, relationships between con-

cepts become more explicit, more precise, and better inte-

grated with other concepts and relationships.

Rote learning and meaningful learning are on opposite

ends of a continuum (Novak, 1998). Some direct fitting of

facts into existing knowledge structure without understand-

ing its relationships may be similar to rote learning.

Schema theory (Rumelhart & Ortony, 1977; Rumelhart &

Norman, 1981a,b; R.C. Anderson, 1984).

Schema theory

posits that knowledge is stored in human memory as

schemas (interconnected concepts and relationships) that are

actively constructed by the learner and organized in a mean-

ingful way. Schema theory implies that sensemaking is

facilitated by prior general knowledge and generic concepts

(about the task and the domain). When new information is

acquired, the sensemaker needs to actively construct a

revised or entirely new knowledge structure. When new

information is ill-fitted (Russell et al., 1993; Klein et al.,

2006b), sensemakers feel internal conflict and need to filter

the data or refine the structure. When the mismatch between

data and structure or the difference between competing

structures is too great, the sensemaker may experience cog-

nitive dissonance which needs to be resolved (Cooper &

Carlsmith, 2001; Festinger, 1957).

Generative learning theory (Wittrock, 1990; Grabowski,

1996).

The learner is actively engaged in the learning

process, working to construct meaningful understanding of

information found in the environment. Much of what is

learned is grounded in the situation where learning takes

place (Lave & Wenger, 1991; J.R. Anderson, Reder, &

Simon, 1996). Two types of meaningful relations are impor-

tant for learning to occur:

• Among the parts of new information and

• Between new information and experience (prior knowledge)

Comprehension

occurs

formulating

connections

between concepts, rather than simply “placing” information

into memory or “transforming” information in memory.

Wittrock (1990) (p. 354, cited from Grabowski, 1996) gives

examples of the results of generative activity: “titles, head-

ings, questions, objectives, summaries, graphs, tables, and

main ideas” (with a focus on “organizational relationships

between different components of the environment”) and

“demonstrations, metaphors, analogies, examples, pictures,

applications, interpretations, paraphrases, and inferences”

FIG. 14.

I-LEARN stages and elements (Neuman, 2011b, p. 97). Reprinted with permission.

FIG. 15.

Inquiry learning model (Alberta Learning. Learning and

Teaching Resources Branch 2004, p. 10). Reprinted with permission.

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(with a focus on the “relationships between the external

stimuli and the memory components”).

Structural Knowledge Acquisition.

Perhaps the central

concept related to learning and sensemaking is structural

knowledge. Structural knowledge plays a key role in the

creation of new understanding or reframing of existing

knowledge. For example, learners who were given the task

of creating a semantic network performed significantly

better on other learning tasks such as relationship judgments

(Jonassen & Wang, 1993).

Structural knowledge supports higher-order thinking in

the form of analogical reasoning. The acquisition of struc-

tural knowledge may be a direct result of structural infor-

mation seeking (Qu & Furnas, 2008) or may be taught, or it

may be a result of generative activities described by the

learning theories discussed in this section. The construction

of personally relevant knowledge structures is the key to

individual sensemaking. Structural knowledge acquisition

improved significantly by focusing the learner’s attention on

structural aspects of the information in the environment.

Visual tools help the acquisition of structures. The best task

performance on analogy was achieved by learners who work

with visual support of a graphical browser and focus on

structural relationships (Jonassen & Wang, 1993).

Types of Conceptual Change

Learning theory postulates types of conceptual changes

in the mental representation of knowledge in learning/

sensemaking. Piaget (1936, 1976) identified two types of

cognitive/conceptual change in knowledge acquisition:

• Assimilation: the addition of information to existing knowl-

edge structures

• Accommodation: the modification or change of existing

knowledge structures

Following Piaget, the schema theorists distinguished

three ways in which existing schemas can be modified by

new experience or information (Rumelhart & Norman,

1981a,b; Vosniadou & Brewer, 1987):

• Accretion (Piaget’s assimilation): the gradual addition of

factual information within existing schemas without schema

change. Accretion may add new data when prior data are

completely missing or fill gaps when prior data are incom-

plete (Chi, 2007).

Piaget’s accommodation is refined according to the

degree of structure change:

• Tuning: Evolutionary conceptual change in the schemas for

organizing and interpreting information, or weak revision, the

modification of existing knowledge structures. These changes

may involve “generalizing or constraining the extent of a

schema’s applicability, determining its default values, or

otherwise improving the accuracy of the schema” to best fit

the data (Vosniadou & Brewer, 1987, p. 52).

• (Radical) restructuring: conceptual changes that involve the

radical change of existing structures or creation of new struc-

tures. Such radical changes often take place when prior

knowledge conflicts with new information. New structures are

constructed either to reinterpret old information or to account

for new information.

Table 3 shows a comparison of conceptual change recog-

nized in the literature.

For accretion to occur, sensemakers need to recognize

how well new information fits into an existing schema in

their knowledge. For weak revisions, the concepts them-

selves are not radically changed; they may be broadened or

their definition otherwise slightly changed. Similarly, the

nature of links may change slightly, and a few connections

may change without changing the basic structure that con-

nects the concepts. Such change can result from the concepts

having acquired more attributes, certain attributes becoming

more or less salient, and so forth. Radical changes or abrupt

changes, on the other hand, occur when new concepts and

relationships are introduced to either complement or replace

the existing concepts and relationships in the knowledge

structure.

Contributions From Cognitive Psychology

This section reviews theories in cognitive psychology

that deal with internal and external representations of

knowledge structure and with the mechanisms that trigger

conceptual changes in such structures.

Knowledge Representations

Choosing

the

right

knowledge

representation

extremely important for sensemaking, problem solving,

and learning. Sensemaking uses internal representations (or

mental models) and external representations (J. Zhang,

1997; Richardson & Ball, 2009), perhaps best viewed as one

TABLE 3.

Types of conceptual change.

Piaget, 1936, 1976

Chi, 2007

Rumelhart & Norman, 1981a

Vosniadou & Brewer, 1987

Assimilation

Adding new knowledge

Accretion

(deals only with structure changes)

Gap filling

Accommodation

Conceptual change

Tuning

Weak-revision

Restructuring

Radical restructuring

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integrated representation consisting of coupled internal and

external parts. Internal representations are constructed by

the sensemaker and worked on through internal mecha-

nisms. External representations are constructed by the

sensemaker or other people or computer programs or the

sensemaker working with computer programs; they are

worked on by the sensemaker or by computer programs

using external mechanisms which are often prescribed. The

processes and representations of sensemaking migrate to

wherever cost is the lowest (Kirsh, 2009). External repre-

sentations may be internalized by the sensemaker and

become part of her or his internal structures, or manipulated

without becoming part of the internal structure. Conversely,

internal representations may be elicited and stored as exter-

nal representations. Tools for externalizing internal repre-

sentations such as graphic organizers (Ausubel et al., 1978)

and tools for generating external representations automati-

cally (computer visualizations) in various application

domains are developed to support sensemaking tasks

(Gaines, 2010) A sensemaking support tool must help

sensemakers to form good representations.

Table 4 shows a classification of types of knowledge

and forms of representations (content and form are not

clearly distinguishable here), which is based primarily on

Rumelhart and Norman (1988). Multiple representational

formats may exist at the same time in the representation

system, for different pieces of knowledge or the same piece

of knowledge (Rumelhart & Norman, 1988).

Different aspects of the world may be represented

through different representational formats that map best into

the sets of operations one wishes to perform. Propositional

representations form the largest part of the knowledge struc-

tures that focus on meaning (Rumelhart & Norman, 1988).

Many theories in cognition (for example, Rumelhart &

Ortony, 1977; Carley & Palmquist, 1992; Jonassen &

Henning, 1996) and many data models and theories of text

structure agree that representations of propositional knowl-

edge can be seen as built from:

• Entities / concepts

• Relationship types, binary or n-ary relationships

• Statements/propositions connecting two or more entities with

a relationship (relationship instances)

Several statements can be connected into maps or net-

works (concept maps, semantic networks). People use map-

like structures to make sense of information (Hoffman,

1992). Schema/frame theory (Wertheimer, 1938; Minsky,

1975, 1977; Rumelhart & Ortony, 1977) views personal

knowledge as stored in schemas that comprise mental

constructs for ideas. A schema is a package of several propo-

sitions, integrated information on a topic. A schema is a

structure template (a frame, the definition of a class in

object-oriented programming). A schema/frame instance is

an instantiated structure (a frame with the slots filled, an

object belonging to a class).

Many sensemaking and problem-solving tasks, such as

comparing products (in order to choose one) or determining

a product price that will maximize profits, require more data

than most people can easily manipulate in their minds, so an

external representation suitable for the sensemaking task at

hand is needed (Faisal, Attfield, & Blandford, 2009). Exter-

nal representations can serve as memory aids to extend

working memory, form permanent archives, and allow

memory to be shared (J. Zhang, 2000; Kirsh, 2010).

There are many forms of external representation: text,

formatted text (as in hierarchical outlines), tabular arrange-

ment of text, diagrams, maps showing relationships between

entities (concept maps, semantic networks), tables and

graphs of numbers, databases, simulation models, photo-

graphs and schematic representation of various objects, just

to name a few. They can be mixed and combined in various

ways. They are suitable, to varying degrees, to present the

different types of knowledge shown in Table 4. Especially

diagrammatic and map representations support cognitive

mechanisms to recognize features more easily and make

inference directly. Spatial, argumentational, faceted, hierar-

chical, sequential, and network representations are seen in

various

sensemaking

tasks

and

sensemaking

support

systems (Ausubel et al., 1978; Faisal et al., 2009). Different

forms of representations are found useful at different stages

of sensemaking (Attfield & Blandford, 2011). There is a vast

literature on the use of external representations in data

analysis and sensemaking, for example, Tufte’s books on

visual display, especially Tufte (2006), Arnheim (1969),

books on visual thinking tools and graphic organizers

(Hyerle, 2008; Hyerle & Alper, 2011), and books on

TABLE 4.

Three-dimensional classification of types of knowledge.

1 Declarative versus procedural knowledge

1.1 Declarative: knowledge about what is (or was or could be)*

1.2 Procedural: knowledge about how to (procedures and processes; J.R. Anderson, 1976, 1983; ten Berge & van Hezewijk, 1999)*

2 Propositional versus analogical knowledge

2.1 Propositional: knowledge structure represented as a set of symbols. Most knowledge stored in long-term memory falls into this category.

Examples include symbolic logic, semantic networks, schemas, and frames*

2.2 Analogical: knowledge structure that has a direct correspondence between the external world and the internal representation, such as a mental

picture of a street view*

3 Implementation of knowledge representations

3.1 Parallel and distributed representations: knowledge structure that is distributed over a large set of units, also called neural networks*

Note. *From Rumelhart and Norman (1988).

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qualitative (e.g., Miles, Huberman, & Saldaña, 2013) and

quantitative research methods and statistical analysis.

Sensemaking Mechanisms

Searching and sensemaking processes involve cognitive

mechanisms that result in the accretion, tuning, and restruc-

turing of knowledge. Researchers in the areas of reasoning

(Toulmin,

Rieke,

Janik,

1979;

Arthur,

1994;

Johnson-Laird, 1999) and reading comprehension (Kavale,

1980), and learning (Vosniadou & Brewer, 1987) reported

mechanisms that are important to the understanding of infor-

mation and creation of knowledge. The mechanisms can be

envisioned as falling into two broad categories: data-driven

(inductive, bottom-up) and structure-driven (logic-driven,

top-down; akin to unsupervised and supervised machine

learning). While, in general, the mechanisms tend to belong

to one category or the other, the distinction between

data-driven and structure-driven mechanisms is not abso-

lute. Some mechanisms may be used in both ways and some

mechanisms may not belong to either category.

Data-driven (inductive) mechanisms involve recognizing

patterns from data and building on the patterns of similarity

and differences to generalize to the abstract structure of

knowledge. In complicated problems where little structured

knowledge is available, sensemakers look for patterns and

use the patterns to construct temporary internal models or

hypotheses or schemas to work with (Arthur, 1994). Primar-

ily data-driven mechanisms include:

• Key item extraction: processing text to identify key concepts

as expressed by words or phrases (Kavale, 1980).

• Schema induction: the discovery of the regularities in the

co-occurrence of certain phenomena (Rumelhart & Norman,

1981a; Vosniadou & Brewer, 1987; Riloff, 1996; Patwardhan

& Riloff, 2006).

• Generalization: making claims about groups based on a

sufficiently representative sample (Toulmin et al., 1979; Chi,

1992).

Structure-driven/logic-driven mechanisms involve using

knowledge schemas and logic to make arguments or reach

conclusions.

Primarily

structure-driven

mechanisms

include:

• Definition: defining different aspects of a concept, such as

purpose, function, and use (Kavale, 1980) or using existing

definitions.

• Specification: specifying as conditions or requirements of a

problem or task (Vosniadou & Brewer, 1987).

• Elimination: eliminating concepts that do not meet certain

criteria in certain attributes (Kavale, 1980).

• Explanation-based mechanisms, or reasoning from cause:

examining

the

causal

connections

two

phenomena

(Toulmin et al., 1979).

• Inference: drawing a conclusion or making a logical judgment

on the basis of available evidence and prior conclusions

(Johnson-Laird, 1999).

Mechanisms spanning both categories: Some mecha-

nisms can be used either bottom-up or top-down:

• Comparison: the comparison of facts, concepts, or relation-

ships (Kavale, 1980)

Ë Similarity: the recognition of common features or attributes

shared by concepts (Vosniadou & Ortony, 1989)

Ë Differentiation or discrimination: the recognition of differ-

ent features of concepts (Vosniadou & Brewer, 1987; Chi,

1992)

• Classification: relating a concept to a broader conceptual cat-

egory and grouping of sufficiently similar concepts (Kavale,

1980); classification is based on comparison. Classification

may be manifested through bottom-up approaches such as

clustering or unsupervised learning, or through top-down

approaches such as assigning items to pre-established catego-

ries or supervised learning.

• Analogy and metaphor: concepts from different domains

that are alike in some ways, especially in the structural

relationships they express, may share common features or

belong to a common abstract category and may exhibit other

common characteristics (Toulmin et al., 1979; Vosniadou &

Ortony, 1989; Gibbs, 2008).

• Semantic fit: examining the reasonableness with which a

concept appears to fit a certain slot as it relates to the meaning

of the knowledge structure as a whole (Kavale, 1980).

Other mechanisms that do not belong to either the data-

driven or logic-driven approach:

• Questioning: asking questions to oneself can be used to assess

knowledge levels; asking questions to others can be an effec-

tive strategy to bridge gaps in knowledge (Flavell, 1979).

• Socratic dialogue: critical dialogue to facilitate the recogni-

tion of inconsistencies in the current schema. Recognition

of anomalies can serve an important function in initiating

schema restructuring (Vosniadou & Brewer, 1987).

Research found that these mechanisms were used in

intelligence analysis (P. Zhang et al., 2008), spatial sense-

making (Wu et al., 2010), and other information-use

scenarios related to decision-making and choice-making

(Savolainen, 2009).

Contributions From Task-Based

Information-Seeking Research

While this literature does not contribute specific elements

of the model, it provides important context for sensemaking.

Sensemaking as an information task is often required for and

carried out in the context of a work task (application task;

Ingwersen, 1992; Ingwersen & Järvelin, 2005; Byström &

Hansen, 2005). Some information tasks require a “meta

information task,” such as making sense of the information

landscape before carrying out a search for information; thus,

in some cases what is said about work tasks in this section

may apply to an information task that is prepared through

a meta information task; in other words, a meta infor-

mation task is to the corresponding information task as an

JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY—September 2014

1749

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information task is to the corresponding work task. Further-

more, the distinction between information task (gathering

information, making sense of information) and work task

(applying information found or applying the sense made in

sensemaking) is somewhat fluid. There are work tasks, such

as many decision-making tasks, that consist primarily of

manipulating information.

Requirements for sensemaking come from the character-

istics of the work task. Ultimately, sensemakers or their

“customers” need to complete a work task based upon sense

made in the sensemaking task. The internal and external

representations constructed during the sensemaking process

need to fit the work task, or they must be updated (Russell

et al., 1993).

Task-based,

information-seeking

research

examined

work tasks with various complexities, such as routine

information-processing tasks that require very little case-by-

case consideration during the task performance; normal

information-processing tasks that require predictable case-

by-case consideration; and decision tasks that are most

complex and require several decisions specific to each case

(Byström & Järvelin, 1995; Vakkari, 1999; Byström, 2002;

Vakkari & Hakala, 2000). Among several work task charac-

teristics discussed in the review paper by Kim and Soergel

(2005), the work tasks that require at least some degree of

sensemaking often involve:

• New situations or problems

• Complex, less structured situations or problems

• A new domain

• An unclear information need

Findings suggest that at different stages of the work

task different types of information (domain information,

general information about how to solve a problem, and

information specific to the work task) were sought; for

example, background information, information that is rel-

evant in general terms, is sought at the beginning (pre-

focus) stage, whereas information that is more specific,

more pertinent to a chosen focus, is used at the end of the

work task (White, 1975; Kuhlthau, 1991, 2004). Sense-

making tools should provide information organization

mechanisms that are flexible enough to support different

stages of a work task.

Research in topical relevance (Huang & Soergel, 2006)

reveals different ways in which a piece of information may

be useful to a work task. For example, a piece of informa-

tion may be useful to the sensemaking task because it pro-

vides information about a similar work task or topic,

allowing the sensemaker to transfer solutions to the work

task at hand.

A Comprehensive Model of the

Cognitive Process and Mechanisms

of Individual Sensemaking

Our comprehensive information-seeking and sensemak-

ing model takes account of the iterative nature of sensemak-

ing and emphasizes the creation of instantiated structure

elements of knowledge. Building on previous sensemaking

models, a few representative information-seeking and

learning models, and theories of cognition and learning, the

model shows the cognitive processes of sensemaking and

provides explanatory power by incorporating the underlying

mechanisms and different types of conceptual change. An

earlier version of the model was presented in P. Zhang et al.

(2008).

Figure 16 shows the elements involved: processes, activi-

ties, mechanisms, and outcomes of sensemaking. These

FIG. 16.

Sensemaker’s toolbox.

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DOI: 10.1002/asi

elements can be combined in many ways; the activities can

be executed in many different sequences, using different

mechanisms, and leading to any of the outcomes.

Figure 17 shows a framework in which often-observed

patterns can be easily identified but which also allows for

many different paths. The sensemaking process consists of

several iterative loops of information seeking and sensemak-

ing. The sensemaker starts with her/his existing knowledge

(or the lack of knowledge) of a problem or work task

situation and ends with an updated conceptual structure,

different parts of which may be updated through accretion,

tuning, or restructuring.

Sensemakers may start with an exploratory search and

identify gaps in the existing knowledge, or identify gaps

directly by analyzing the problem or planning the work task.

Exploratory search is the pre-focus stage of seeking for

information. During this process, sensemakers identify a

problem, realize they need more information, and, through

exploring or browsing or broad search, learn about what

information they need to update their knowledge. During

exploratory search, sensemakers may look for both data and

structure and move through the structure loop and data loop

in an embryonic form.

Focused search is a process in which sensemakers search

for information about specific aspects of the work task

situation, having specific questions in mind. The questions

represent the gaps identified through problem analysis and

through exploratory search and browsing.

The identification of gaps occurs at various stages with

different levels of specificity. At the very beginning, the

identified gap is a loose notion of lack of knowledge on

some topic or problem. As searching and sensemaking con-

tinue, more specific gaps may be identified, including data

gaps and structure gaps.

If a structure gap is identified, sensemakers may, in

varying proportions:

• Search for structures created and described by others and put

together a structure from what they found combined with

what they already know.

• Examine the relationships of various parts of the internal

structures in their existing knowledge, look for patterns in the

data, and build their own structure or structure modification.

If a data gap is identified, the sensemaker conducts

focused search looking for the particular pieces of data, and

fits the data found into the previously built structure (instan-

tiating representations). If the data needed are not available

from an existing source, the sensemaker needs to either do

without the data or conduct original data collection.

There are two mini-loops involved: the data loop and the

structure loop (depending on the focus of a particular itera-

tion of the sensemaking process), both of which are embed-

ded in a larger loop of sensemaking in which knowledge is

consistently updated. Sensemakers may take various paths,

and the loops may be closely intertwined.

FIG. 17.

A comprehensive model of the cognitive process and mechanisms of individual sensemaking.

JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY—September 2014

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Information seeking/search, whether for data or for struc-

ture, can be characterized from three perspectives:

1. Scanning and monitoring the environment versus specific

search for information from the environment triggered by

problems or work tasks at hand, possibly guided by a

representation that needs to be instantiated with data, a

representation derived from analyzing the work task.

2. Pre-focus, exploratory search and browsing for structure

and data versus focused search.

3. Searching for sources versus searching in sources (infor-

mation extraction).

Instantiating structures may result in accretion (the data fit

with the existing structure), or in tuning (the sensemaker

makes minor modifications on the structure so it fits the data),

or in radical restructuring. Using structures created by others

(usually found through a search for structures) may result in

tuning (the gradual change in knowledge structure) or in

restructuring (the radical change in knowledge structure).

Different pieces of the structure may remain unchanged or

changed through accretion, tuning, or restructuring.

Some sensemakers may start top-down, create structures

and then search for data to fit in; others may start bottom-up

from the data, and any changes in the structure may be

accumulated from observing new data. Accumulated accre-

tion may result in tuning, and accumulated tuning may result

in restructuring.

Sensemaking activities use several mechanisms, each

serving different functions in the structuring of a conceptual

space. The bottom of Figure 17 gives a preliminary list com-

piled from the literature amended with findings from an

empirical user study (P. Zhang, 2010). Cognitive mecha-

nisms may be used alone or in combination. For example, a

sensemaker may use the key item extraction mechanism to

extract key entities/concepts and relationships as the basic

structure elements to build on. He or she may then use

specification to specify different aspects or requirements of

an extracted concept. Both data-driven mechanisms and

structure-driven mechanisms serve to come up with or vali-

date structure; they belong to the structure loop.

We applied this model in an empirical study of sense-

making processes (P. Zhang, 2010). The model proved

helpful in understanding sensemaking processes of journal-

ism and business students working on news writing and

business plan development assignments, respectively. The

analysis of these sensemaking processes led to many refine-

ments of the model.

The ultimate product of sensemaking is an updated

knowledge representation, which consists of instantiated

structures (or schemas). The mechanisms described earlier

influence the creation of instantiated structures and the

knowledge update. The sensemaker incorporates the rel-

evant data found in information seeking in the schema; put

differently, the final schema accounts for the relevant data.

Once the sensemaker incorporates the instantiated structures

into his/her existing knowledge and possibly produced a

report, the sensemaking is accomplished.

Limitations of the Model

The framework and model presented in Table 1 and

Figure 17 is the result of a sensemaking process carried out

by the authors working on the inputs discussed. Such theo-

retical analysis depends always on who does it; thus, our

model may be limited by our ability to interpret the input

models, clearly identify their component elements, and

arrange these elements in an integrated structure that makes

sense. This limitation could be overcome by a consensus

process among a larger group, but this would be well beyond

our means for this analysis.

There are other limitations as well. Sensemaking is often

a social process, but our model is limited to individual sen-

semaking. While affect clearly plays an important role in

sensemaking, we focus strictly on the cognitive component.

Our model is based primarily on secondary analysis, to a

much lesser extent on our own empirical work. It is limited

by the selection of input models; models we missed may

have contributed additional aspects, especially to Table 1.

The model needs to be applied in many different situations

to establish its usefulness. Perhaps a general model is

not possible, only models for specific sensemaking tasks.

Sensemaking in specific practice areas may follow certain

patterns (i.e., sequences of steps that sensemakers go

through) and the general model may not be most useful in

analyzing task-specific activities.

Conclusions and Discussion

The model proposed in this paper provides a framework

for analyzing and describing the cognitive process and

mechanisms of individual sensemaking; it focuses on the

changes to the conceptual space and the cognitive mecha-

nisms used in achieving these changes. It affords a better

understanding of sensemaking and provides a basis for:

• Empirical studies: Table 1 can be used as a scheme for coding

the elements of a sensemaking process that is observed. The

comprehensive model presented in Figure 17 can guide a

researcher in producing process diagrams to analyze user

processes and activities.

• Interpreting empirical user studies and integrating their

results using the framework established in Table 1 and

Figure 17.

• Education in critical thinking and sensemaking skills. Stu-

dents from an early age can be taught an overall approach to

sensemaking that includes the elements and subprocesses of

sensemaking most suited for the sensemaking task at hand.

They can be taught the general flow with the knowledge that

they often need to go back and forth. They can be taught

which approach (for example, top-down or bottom-up) is

most appropriate in a given situation or discover which

approach is best suited for their own cognitive style.

• The design of sensemaking assistant tools that support and

guide the users—from K–12 students to university students to

practitioners and researchers at the highest levels—in arrang-

ing data and thoughts and creating a wide range of external

representations and provide automated support for some

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DOI: 10.1002/asi

functions, such as information extraction from text, visualiza-

tion of relationships between entities and of quantitative data,

automatic inference, and conversion from one representation

to another, for example, from a concept map to an outline.

The framework also sheds light on research in informa-

tion seeking and use from a perspective of the creation of

structured representations. The processes and cognitive

mechanisms identified provide better foundations for knowl-

edge creation, organization, and sharing practices. By char-

acterizing the overall process of sensemaking as iterations

of “information seeking–sensemaking” that are linked with

iterative updates of the conceptual space triggered by a set of

cognitive mechanisms, the model shows how sensemakers

move along from one knowledge state to the next, and what

requisites are needed to enable such movements.

Researchers in LIS have been studying task-based infor-

mation seeking and use, and they made a useful distinction

between information task and work task (Vakkari & Hakala,

2000; Byström, 2002). Sensemaking, as an information task,

is needed for many work tasks, such as problem solving and

decision making. The representations constructed during the

sensemaking process need to fit the task, or they must be

updated (Russell et al., 1993). In fact, examining informa-

tion use as gap-bridging under a sensemaking framework

provides

insights

information-behavior

research

(Savolainen, 2006). Task-based, information-seeking and

use research can benefit from the analysis of creation and

use of structured representations for tasks and problems. In

addition to the compound nature of information tasks and

work tasks, the examination of the concepts and relation-

ships in the knowledge space of users suggested that task

structure and topic structure are often intertwined and work

together to best serve the functional demands of the task.

Researchers have sometimes avoided talking about inter-

nal representations and cognitive aspects of sensemaking

because of the difficulty in assessing what is in a user’s mind

(Cacioppo & Petty, 1981; Chi, 2006; Das, Naglieri, & Kirby,

1994) and the limitations of using verbal reports and obser-

vations as data to interpret mental process (Nisbett &

Wilson, 1977; Ericsson & Simon, 1993; Hoffman, Shadbolt,

Burton, & Klein, 1995). However, the cognitive processes

and mechanisms are fundamental to information-behavior

research and need more attention in the field.

Tools have been developed to support sensemaking in

various ways, mostly to capture intermediate products of

sensemaking such as insights (Gersh, Lewis, Montemayor,

Piatko, & Turner, 2006) and analytical thoughts (Lowrance,

Harrison, & Rodriguez, 2001), and to provide a workspace

of the intermediate representations (Wang & Haake, 1997;

Hsieh

Shipman,

2002;

Wright,

Schroh,

Proulx,

Skaburskis, & Cort, 2006). However, there is less support

for connecting intermediate products to the conceptual

structure that users develop. A sensemaking tool should

support the ability to “flexibly arrange, re-arrange, group,

and name and re-name groups” (Hearst, 2009, p. 169) of raw

information and intermediate products of sensemaking. The

comprehensive

information-seeking

and

sensemaking

model provides the basis for developing sensemaking tools

that support users’ structuring a conceptual space using

various sources, including search results and intermediate

structured representations (which can themselves be part

of the concept space) such as concept maps, templates,

and outlines. With the growth of computer-based informa-

tion systems, computer-generated displays as external

representation can help the quality of complex information-

processing tasks for many types of tasks. Much prior

work on the role of external representations in individual

problem solving used well-structured problems. Further

studies

need

investigate

ill-structured,

open-ended

problems.

We need good sensemaking assistant tools to help users

learn and to make best use of large amounts of information.

To design such tools we need a better understanding of the

sensemaking process, both of what people actually do and

what they should do to achieve better results. Our compre-

hensive model of the individual cognitive processes points

toward arriving at such an understanding. The model would

be even more useful if it were augmented to consider affec-

tive, social, and organizational factors; we are interested in

working with others on such an expansion.

Acknowledgment

We were formerly at the College of Information Studies,

University of Maryland. This work was partially supported

by the Eugene Garfield Dissertation Fellowship from the

Beta Phi Mu Library & Information Studies Honor Society.

The first author also received support from the Beijing Key

Discipline Construction Fund. We thank the editor and two

anonymous reviewers for their constructive comments,

which helped us to significantly improve the article.

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Supporting Information

Additional Supporting Information may be found in the

online version of this article at wileyonlinelibrary.com:

Appendix A

classification of sensemaking activities

linked to a number of sensemaking models.

Table A-1.

Elements of Sensemaking. A faceted classi-

fication (expanded version of Table 1).

Table A-2.

Sensemaking, search, and learning models

linked to sensemaking activities classified in Table A-1.

Table A-3.

The sensemaking activities classified in

Table A-1 linked to the elements of sensemaking, search,

and learning models (a much expanded version of Table 2).

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DOI: 10.1002/asi